BACKGROUND: Simulation-based training has gained distinction in cardiothoracic surgery as robotic-assisted cardiac procedures evolve. Despite the increasing use of wet lab simulators, the effectiveness of these training methods and skill acquisition rates remain poorly understood. OBJECTIVES: This study aimed to compare learning curves and assess the robotic cardiac surgical skill acquisition rate for cardiac and noncardiac surgeons who had no robotic experience in a wet lab simulation setting. METHODS: In this prospective cohort study, participants practiced 3 robotic tasks in a porcine model: left atriotomy closure, internal thoracic artery harvesting and mitral annular suturing. Participants were novice robotic cardiac and noncardiac surgeons alongside experienced robotic cardiac surgeons who established performance benchmarks. Performance was evaluated using the time-based score and modified global evaluative assessment of robotic skills (mGEARS). RESULTS: The participants were 15 novice surgeons (7 cardiac; 8 noncardiac) and 4 experienced robotic surgeons. Most novices reached mastery in 52 (±22) min for atrial closure, 32 (±18) for internal thoracic artery harvesting and 34 (±12) for mitral stitches, with no significant differences between the cardiac and noncardiac surgeons. However, for mGEARS, noncardiac novices faced more challenges in internal thoracic artery harvesting. The Thurstone learning curve model indicated no significant difference in the learning rates between the groups. CONCLUSIONS: Wet lab simulation facilitates the rapid acquisition of robotic cardiac surgical skills to expert levels, irrespective of surgeons' experience in open cardiac surgery. These findings support the use of wet lab simulators for standardized, competency-based training in robotic cardiac surgery.

Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares Madrid Spain

Copenhagen Academy for Medical Education and Simulation Center for HR and Education Copenhagen Denmark

Department of Cardiac Surgery Ospedale San Carlo di Nancy Rome Italy

Department of Cardiothoracic Surgery Aalborg University Hospital Aalborg Denmark

Department of Cardiothoracic Surgery Amsterdam University Medical Center Amsterdam the Netherlands

Department of Cardiothoracic Surgery Leiden University Medical Center Leiden the Netherlands

Department of Cardiovascular Surgery Hospital Clínic Barcelona Spain

Department of Cardiovascular Surgery University Hospital Motol Prague Czech Republic

Department of Clinical Medicine Aalborg University Aalborg Denmark

Department of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark

Department of Obstetrics Copenhagen University Hospital Rigshospitalet Copenhagen Denmark

Division of Cardiac Surgery Department of Surgery Western University London ON Canada

Gastrounit Surgical Section Copenhagen University Hospital Amager and Hvidovre Hvidovre Denmark

Nordsim Aalborg University Hospital Aalborg Denmark

ROCnord Robotic Center Aalborg Aalborg University Hospital Aalborg Denmark

Unit of Clinical Biostatistics Aalborg University Hospital Aalborg Denmark

See more in PubMed

Haidari TA, Nayahangan LJ, Bjerrum F, et al.; Participants Delphi. Consensus on technical procedures for simulation-based training in thoracic surgery: an international needs assessment. Eur J Cardiothoracic Surg 2023;63:1–9. PubMed

Zientara A, Hussein N, Bond C. et al. Basic principles of cardiothoracic surgery training: a position paper by the European Association for Cardiothoracic Surgery Residents Committee. Interact Cardiovasc Thorac Surg 2022;35:ivac213. PubMed PMC

Wang C, Karl R, Sharan L. et al. Surgical training of minimally invasive mitral valve repair on a patient-specific simulator improves surgical skills. Eur J Cardiothorac Surg 2024;65:ezad387. PubMed

Whittaker G, Salmasi MY, Aydin A. et al. Recommendations for the use of coronary and valve simulators in cardiac surgical training: a systematic review. Eur J Cardiothorac Surg 2021;61:1–10. PubMed

Palmen M, Navarra E, Bonatti J. et al. Current state of the art and recommendations in robotic mitral valve surgery. Interact Cardiovasc Thorac Surg 2022;35:ivac160. PubMed PMC

Balkhy HH, Nisivaco S, Kitahara H. et al. Robotic off-pump totally endoscopic coronary artery bypass in the current era: report of 544 patients. Eur J Cardiothorac Surg 2022;61:439–46. PubMed

Badhwar V, Pereda D, Khaliel FH. et al. Outcomes following initial multicenter experience with robotic aortic valve replacement: defining a path forward. J Thorac Cardiovasc Surg 2024;167:1244–50. PubMed

Almousa A, Mehaffey JH, Wei LM. et al. Robotic-assisted cryothermic Cox maze for persistent atrial fibrillation: longitudinal follow-up. J Thorac Cardiovasc Surg 2023;165:1828–36.e1. PubMed

Torregrossa G, Amabile A, Oosterlinck W. et al. The epicenter of change: robotic cardiac surgery as a career choice. J Card Surg 2021;36:3497–500. PubMed

Badhwar V, Wei LM, Geirsson A. et al. Contemporary robotic cardiac surgical training. J Thoracic Cardiovasc Surg 2023;165:779–83. PubMed

Gillinov M, Mick S, Mihaljevic T, Suri RM.. Watch one, do one, teach one. J Thorac Cardiovasc Surg 2016;151:1506–7. PubMed

Pusic MV, Boutis K, Hatala R, Cook DA.. Learning curves in health professions education. Acad Med 2015;90:1034–42. PubMed

Krebs ED, Chancellor WZ, Hawkins RB. et al. Objective measure of learning curves for trainees in cardiac surgery via cumulative sum failure analysis. J Thorac Cardiovasc Surg 2020;160:460–6.e1. PubMed

Valsamis EM, Chouari T, O’Dowd-Booth C, Rogers B, Ricketts D.. Learning curves in surgery: variables, analysis and applications. Postgrad Med J 2018;94:525–30. PubMed

Jonsson A, Binongo J, Patel P. et al. Mastering the learning curve for robotic-assisted coronary artery bypass surgery. Ann Thorac Surg 2023;115:1118–25. PubMed

Yaffee DW, Loulmet DF, Kelly LA. et al. Can the learning curve of totally endoscopic robotic mitral valve repair be short-circuited? Innovations (Phila) 2014;9:43–8. PubMed

Goodman A, Koprivanac M, Kelava M. et al. Robotic mitral valve repair: the learning curve. Innovations (Phila) 2017;12:390–7. PubMed

Ashraf SF, Seese L, Hasan I, Coyan G, Turner M, Bonatti J.. Learning Curve and Confidence Increase of Residents Practising Annuloplasty Stitches in a Porcine Wet-Lab Model of Mitral Valve Repair. American Association for Thoracic Surgery, 2023. https://www.aats.org/resources/abstract.pdf?abstract=1250081

Rodriguez E, Nifong LW, Bonatti J. et al. Pathway for surgeons and programs to establish and maintain a successful robot-assisted adult cardiac surgery program. J Thorac Cardiovasc Surg 2016;152:9–13. PubMed

Gómez-Hernández MT, Fuentes MG, Novoa NM, Rodríguez I, Varela G, Jiménez MF.. The robotic surgery learning curve of a surgeon experienced in video-assisted thoracoscopic surgery compared with his own video-assisted thoracoscopic surgery learning curve for anatomical lung resections. Eur J Cardiothorac Surg 2022;61:289–96. PubMed

Pietersen PI, Hertz P, Olsen RG, Møller LB, Konge L, Bjerrum F.. Transfer of skills between laparoscopic and robot-assisted surgery: a systematic review. Surg Endosc 2023;37:9030–42. PubMed

Atroshchenko GV, Navarra E, Valdis M. et al. Simulation-based assessment of robotic cardiac surgery skills: an international multicenter, cross-specialty trial. JTCVS Open 2023;16:619–27. PubMed PMC

Goh AC, Goldfarb DW, Sander JC, Miles BJ, Dunkin BJ.. Global evaluative assessment of robotic skills: validation of a clinical assessment tool to measure robotic surgical skills. J Urol 2012;187:247–52. PubMed

Tang DHY, Østdal TB, Vamadevan A. et al. No difference between using short and long intervals for distributed proficiency-based laparoscopy simulator training: a randomized trial. Surg Endosc 2024;38:300–5. PubMed PMC

Pusic MV, Boutis K, Pecaric MR, Savenkov O, Beckstead JW, Jaber MY.. A primer on the statistical modelling of learning curves in health professions education. Adv Health Sci Educ Theory Pract 2017;22:741–59. PubMed

Schachner T, Bonaros N, Wiedemann D. et al. Training surgeons to perform robotically assisted totally endoscopic coronary surgery. Ann Thorac Surg 2009;88:523–7. PubMed

Patrick WL, Iyengar A, Han JJ. et al. The learning curve of robotic coronary arterial bypass surgery: a report from the STS database. J Card Surg 2021;36:4178–86. PubMed PMC

Chahal B, Aydin A, Ahmed K.. Virtual reality vs. physical models in surgical skills training. An update of the evidence. Curr Opin Urol 2024;34:32–6. PubMed PMC

Valdis M, Chu MWA, Schlachta C, Kiaii B.. Evaluation of robotic cardiac surgery simulation training: a randomized controlled trial. J Thorac Cardiovasc Surg 2016;151:1498–505.e2. PubMed

Toolan C, Palmer K, Al-Rawi O, Ridgway T, Modi P.. Robotic mitral valve surgery: a review and tips for safely negotiating the learning curve. J Thorac Dis 2021;13:1971–81. PubMed PMC

Halkos ME, Jonsson A, Badhwar V. et al. (2024) Developing proficiency in robotic cardiac surgery. Ann Thorac Surg, 10.1016/j.athoracsur.2024.07.049 PubMed DOI

Vinck EE, Smood B, Barros L, Palmen M.. Robotic cardiac surgery training during residency: preparing residents for the inevitable future. Laparosc Endosc Robot Surg 2022;5:75–7.

Gillinov AM, Mihaljevic T, Javadikasgari H. et al. Early results of robotically assisted mitral valve surgery: analysis of the first 1000 cases. J Thorac Cardiovasc Surg 2018;155:82–91.e2. PubMed

Oosterlinck W, Gianoli M, Palmen M. et al. European Association of Cardiothoracic Surgeons future view on robotic cardiac surgery in Europe. Eur J Cardiothorac Surg 2024;66:ezae339. PubMed